Fitting of two benthic fishes abundance Paracobitis hircanica and Neogobius fluviatilis Using Fuzzy Regression

Knowledge of biological requirements of aquatic organisms in perennial rivers is necessary for protection of biological diversity in riverine ecosystems. Habitat knowledge includes using models for estimating specific properties from physical structure of rivers by simple and accurate methods. Fuzzy regression model was investigated for two common endemic and benthic fishes, Paracobitis hircanica and Neogobius fluviatilis, in Zarin Gol River of northern Iran. P.hircanica and N.fluviatilis overlapped in mesohabitat use, both fish preferred riffle to pools. Further analyses based on stepwise multiple regressions showed that fish abundance was significantly correlated with riparian vegetation and substrate for P.hircanica, but correlated with water depth, stream width, velocity, and discharge for N.fluviatilis. The obtained results showed that fuzzy regression can be an appropriate complementary or alternative method for statistical regression when the relationship between the variables is vague or there are errors due to vagueness in regression equation structure. The result recommends that the various substratum compositions may have accounted for the co-existence of these two benthic fishes. It would also provide important information for habitat management and ecological engineering of mountain rivers in Iran.